Introduces the concept of shortest path, discussing weighted paths, Hamiltonian paths, and path optimization algorithms.
Introduces induced flows, basis matrices, and tree solutions in graph theory.
Explores worst-case complexity in computer science and the importance of real-life complexity in algorithm selection.
Covers the max flow problem, weak duality, and the Ford-Fulkerson algorithm.
Explores the Ford Fulkerson algorithm, Max-Flow Min-Cut theorem, Incidence matrix, and network optimization complexity.
Introduces linear programming basics, including optimization problems, cost functions, simplex algorithm, geometry of linear programs, extreme points, and degeneracy.
Covers finding the shortest path in directed graphs efficiently using algorithmic approaches and discussing related NP-complete problems.
Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.
